Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

Contrastive learning (CL) aims to learn useful representation without relying on expert
annotations in the context of medical image segmentation. Existing approaches mainly …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in the context of medical image segmentation. Existing methods …

Pairwise image registration is a necessary prerequisite for brain image comparison and data
integration in neuroscience and radiology. In this work, we explore the efficacy of implicit …

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and
implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR …

We propose a novel image registration method based on implicit neural representations that
addresses the challenging problem of registering a pair of brain images with similar …

This work proposes NePhi, a generalizable neural deformation model which results in
approximately diffeomorphic transformations. In contrast to the predominant voxel-based …

The Orientation Distribution Function (ODF) characterizes key brain microstructural
properties and plays an important role in understanding brain structural connectivity. Recent …

This work proposes NePhi, a generalizable neural deformation model which results in
approximately diffeomorphic transformations. In contrast to the predominant voxel-based …